Numerical modelling of desiccation cracking

The ability to model and predict the formation of desiccation cracks is potentially beneficial in many applications such as clay liner design, earth dam construction, and crop science, etc. However, most studies have focused on statistical analysis of crack patterns and qualitative study of contributing factors to crack development rather than prediction. Because it is exceedingly difficult to capture the nonlinear processes during desiccation in analytical modelling, most such models handle crack formation without considering variation of material properties with time, and are unattractive to use in realistic modelling. The data obtained from laboratory experiments on clay soil desiccating in moulds were used as a basis to develop a more refined model of desiccation cracking. In this study, the properties, such as matric suction, stiffness and tensile strength of soil, and base adhesion, could be expressed approximately as functions of moisture content. The initial conditions and the development of suction due to desiccation and the varying material properties were inputted to UDEC, a distinct element code, using its internal programming language FISH. The model was able to capture some essential physical aspects of crack evolution in soil contained in moulds with varying lengths, heights, and materials of construction. Extension of this methodology is potentially beneficial not only for modelling desiccation cracking in clay, but also in other systems with evolving material properties such as concrete structures and road pavements. Copyright © 2010 John Wiley & Sons, Ltd.     

Generation of continuous surface soil moisture dataset using combined optical and thermal infrared images

Surface soil moisture (SSM) is a critical variable for understanding water and energy flux between the atmosphere and the Earth's surface. An easy to apply algorithm for deriving SSM time series that primarily uses temporal parameters derived from simulated and in situ datasets has recently been reported. This algorithm must be assessed for different biophysical and atmospheric conditions by using actual geostationary satellite images. In this study, two currently available coarse‐scale SSM datasets (microwave and reanalysis product) and aggregated in situ SSM measurements were implemented to calibrate the time‐invariable coefficients of the SSM retrieval algorithm for conditions in which conventional observations are rare. These coefficients were subsequently used to obtain SSM time series directly from Meteosat Second Generation (MSG) images over the study area of a well‐organized soil moisture network named REMEDHUS in Spain. The results show a high degree of consistency between the estimated and actual SSM time series values when using the three SSM dataset‐calibrated time‐invariable coefficients to retrieve SSM, with coefficients of determination (R²) varying from 0.304 to 0.534 and root mean square errors ranging from 0.020 m³/m³ to 0.029 m³/m³. Further evaluation with different land use types results in acceptable debiased root mean square errors between 0.021 m³/m³ and 0.048 m³/m³ when comparing the estimated MSG pixel‐scale SSM with in situ measurements. These results indicate that the investigated method is practical for deriving time‐invariable coefficients when using publicly accessed coarse‐scale SSM datasets, which is beneficial for generating continuous SSM dataset at the MSG pixel scale.     

Temporal stability of soil moisture in an oasis of northwestern China

Temporal stability of soil moisture has been widely used in hydrological monitoring since it emerged. However, the spatial analysis of temporal stability at the landscape scale is often limited because of insufficient sampling numbers. This work made an effort to investigate the spatial variations of temporal stability of soil moisture in an oasis landscape. The specific objectives of the study were to explore the spatial patterns of temporal stability and to determine the controlling factors of temporal stability in the desert oasis. A time series of soil moisture measurements were gathered on 23 occasions at 118 locations over 3 years in a rectangular transect of approximately 100 km². The nonparametric Spearman's rank correlation coefficient, standard deviation of relative difference (SDRD), and mean absolute bias error (MABE) were used to quantify the temporal stability of soil moisture. Results showed that the temporal stability of soil moisture was depth dependent and season dependent. The spatial pattern of soil moisture in a deep soil layer and between two same seasons generally had a high temporal stability. SDRD and MABE were spatially autocorrelated and exhibited strong spatial structures in the geographic space. The concept of temporal stability can be extended to describe the time‐stable areas of soil moisture with geostatistics. There were great differences between SDRD and MABE in describing the temporal stability of soil moisture and in identifying the controlling factors of temporal stability. In this case, MABE was a better alternative to estimate the areal mean soil moisture using representative locations than SDRD. Land use type, soil moisture condition, and soil particle composition were the dominant controls of temporal stability in the oasis. These insights could help to better understand the essence of temporal stability of soil moisture in arid regions.     

Revised runoff curve number for runoff prediction in the Loess Plateau of China

The soil conservation service (now Natural Resources Conservation Service) Curve Number (SCS-CN), one of the most commonly used methods for surface runoff prediction. The runoff calculated by this method was very sensitive to CN values. In this study, CN values were calculated by both arithmetic mean (CN_C) and least square fit method (CN_F) using observed rainfall-runoff data from 43 sites in the Loess Plateau region, which are considerably different from the CN₂ values obtained from the USDA-SCS handbook table (CN_T). The results showed that using CN_C instead of CN_T for each watershed produce little improvement, while replacing CN_T with CN_F improves the performance of the original SCS-CN method, but still performs poorly in most study sites. This is mainly due to the SCS-CN method using a constant CN value and discounting of the temporal variation in rainfall-runoff process. Therefore, three factors—soil moisture, rainfall depth and intensity—affecting the surface runoff variability are considered to reflect the variation of CN in each watershed, and a new CN value was developed. The reliability of the proposed method was tested with data from 38 watersheds, and then applied to the remaining five typical watersheds using the optimized parameters. The results indicated that the proposed method, which boosted the model efficiencies to 81.83% and 74.23% during calibration and validation cases, respectively, performed better than the original SCS-CN and the Shi and Wang (2020b) method, a modified SCS-CN method based on tabulated CN value. Thus, the proposed method incorporating the influence of the temporal variability of soil moisture, rainfall depth, and intensity factors suggests an accurate runoff prediction for general applications under different hydrological and climatic conditions on the Loess Plateau region.     

Tropical precipitation anomalies and d‐excess evolution during El Niño 2014‐16

The last 2014‐16 El Niño event was among the three strongest episodes on record. El Niño considerably changes annual and seasonal precipitation across the tropics. Here, we present a unique stable isotope data set of daily precipitation collected in Costa Rica prior to, during, and after El Niño 2014‐16, in combination with Lagrangian moisture source and precipitation anomaly diagnostics. δ²H composition ranged from ‐129.4 to +18.1 (‰) while δ¹⁸O ranged from ‐17.3 to +1.0 (‰). No significant difference was observed among δ¹⁸O (P=0.186) and δ²H (P=0.664) mean annual compositions. However, mean annual d‐excess showed a significant decreasing trend (from +13.3 to +8.7 ‰) (P<0.001) with values ranging from +26.6 to ‐13.9 ‰ prior to and during the El Niño evolution. The latter decrease in d‐excess can be partly explained by an enhanced moisture flux convergence across the southeastern Caribbean Sea coupled with moisture transport from northern South America by means of an increased Caribbean Low Level Jet regime. During 2014‐15, precipitation deficit across the Pacific domain averaged 46% resulting in a very severe drought; while a 94% precipitation surplus was observed in the Caribbean domain. Understanding these regional moisture transport mechanisms during a strong El Niño event may contribute to a) better understanding of precipitation anomalies in the tropics and b) re‐evaluate past stable isotope interpretations of ENSO events in paleoclimatic archives within the Central America region.     

Monitoring over‐winter soil water dynamics in a freezing and snow‐covered environment using a thermally insulated tensiometer

Water potential below a frozen soil layer was continuously monitored over an entire winter period (using thermally insulated tensiometers sheltered in a heated chamber) along with other soil, snow and atmospheric variables. In early winter, the freezing front advanced under a thin snow cover, inducing upward soil water flow in the underlying unfrozen soil. The freezing front started to retreat when the snow cover became thick enough to insulate the soil, resulting in the reversal of the flow direction in the unfrozen zone. These data provide a clear illustration of soil water dynamics, which have rarely been monitored with a tensiometer. Copyright © 2005 John Wiley & Sons, Ltd.     

基于温度植被干旱指数的电网输电线路走廊干旱分析

以湖北省输电线路走廊地区作为研究区,利用2013年1~9月MODIS卫星影像数据,处理得到月尺度的归一化植被指数(Normalized Differential Vegetation Index,NDVI)与地表温度(Land Surface Temperature,LST)数据,构建NDVI-Ts特征空间,计算得到温度植被干旱指数(Temperature Vegetation Dryness Index,TVDI),用TVDI监测结果分析湖北省输电线路走廊区域2013年干旱时空分布情况。结果表明,湖北省输电线路走廊地区TVDI和土壤含水量之间存在显著的负相关,相关系数达到0.525(p0.05),由MODIS卫星影像计算得到TVDI影像可以有效表明湖北省输电线路走廊地区的土壤含水情况。     

中国夏季气温对东亚土壤湿度异常响应的统计评估

基于欧亚夏季土壤湿度变化特征及其与中国夏季气温的相关分析,选取东亚地区作为土壤湿度异常影响中国夏季气温的陆面关键区,采用广义平衡反馈分析方法(GEFA)探讨了我国夏季气温对东亚地区土壤湿度异常的可能响应,并初步讨论了相关的物理过程。结果表明:中国夏季气温与东亚地区初夏和同期的土壤湿度异常具有密切的联系;进一步分析表明,夏季气温距平场对土壤湿度第一模态的响应最显著:当东亚中纬度及我国东部地区土壤湿度异常偏干时,夏季气温表现为一致增暖;而土壤湿度第二模态对长江流域至我国西部地区的气温有较弱的强迫作用;气温对第三模态的响应主要表现为华南地区的显著降温。并以对气温影响最为显著的土壤湿度异常第一模态为例,初步探讨了气温对土壤湿度异常响应的可能物理过程。当贝加尔湖以南以及我国东部的土壤偏干时,地表异常加热容易引起我国北方高层大气出现明显正异常和低层的反气旋性异常环流,上述环流异常容易导致温度偏高,同时不利于该区域降水的发生,进而导致土壤湿度偏低,上述正反馈机制可能是该区域土壤湿度与大气之间联系的一种可能途径。     

基于自动观测站的鲁中地区土壤水分变化规律及精细化预报模型的研究

为了建立鲁中地区土壤水分精细化预报模型,利用2010—2013年农田土壤水分自动站逐日资料进行土壤水分年、月变化特征研究,并结合附近自动气象站资料,以土壤水分平衡方程、农田蒸散模型为基础,采用逐步回归和曲线估计等方法建立4—6月无降水条件下平原水浇田与山旱田土壤水分1 d、7 d降幅的经验预报模型。结果表明:鲁中地区0~100 cm土壤水分贮存量年变化趋势和0~50 cm基本一致,年最高出现在8月,最低出现在6月,年降幅最大出现在3—6月,易出现干旱。对预报模型进行回代和预报检验结果显示,回代平均相对误差为0.07%,7 d模型和1 d模型滚动预报第7天0~50 cm土壤水分贮存量,绝对误差分别为-0.15和-2.17 mm,平均相对误差分别为-0.07%和-1.56%,模型具有较强的理论基础和实用性,预报精度较高,为鲁中地区土壤墒情监测和精细化预报提供支持。     

FDR土壤水分传感器原状土壤标定方法研究

为获得测站准确的土壤水分传感器标定参数,利用自动土壤水分站点的大型原状土壤为样本,在实验室同时进行人工称重观测和仪器自动对比观测,得到一系列的人工自动测量数据,以人工称重数据为准对自动土壤水分传感器进行订正。由于采用原状土壤,样本土壤的质地、密度、土壤颗粒和结合紧密度基本没有受到破坏,订正后的自动土壤体积含水量值能更加真实反映台站实际土壤墒情,能够修订人工对比观测期间由于样本空间不足导致的不合理的田间标定方程。